Comment from the Stata technical group

Maximum Likelihood Estimation with Stata, Fourth Edition is the essential
reference and guide for researchers in all disciplines who wish to write
maximum likelihood (ML) estimators in Stata. Beyond providing comprehensive
coverage of Stata’s ml command for writing ML estimators, the
book presents an overview of the underpinnings of maximum likelihood and how
to think about ML estimation.

The book shows you how to take full advantage of the ml command’s
noteworthy features:

linear constraints

four optimization algorithms (Newton–Raphson, DFP, BFGS, and BHHH)

observed information matrix (OIM) variance estimator

outer product of gradients (OPG) variance estimator

Huber/White/sandwich robust variance estimator

cluster–robust variance estimator

complete and automatic support for survey data analysis

direct support of evaluator functions written in Mata

When appropriate options are used, many of these features are provided
automatically by ml and require no special programming or
intervention by the researcher writing the estimator.

The fourth edition has been updated to include new features introduced in
recent versions of Stata. Such features include new methods for handling scores, more
consistent arguments for likelihood-evaluator programs,
and support for likelihood evaluators written in
Mata (Stata’s matrix programming language). The authors illustrate how to
write your estimation command so that it fully supports factor-variable
notation and the svy prefix for estimation with survey data. They
have also restructured the chapters that introduce ml in a way that
allows you to begin working with ml faster. This edition is essential for anyone using Stata 11.

In the final chapter, the authors illustrate the major steps required to get
from log-likelihood function to fully operational estimation command. This
is done using several different models: logit and probit, linear regression,
Weibull regression, the Cox proportional hazards model, random-effects
regression, and seemingly unrelated regression.

The authors provide extensive advice for developing your own estimation
commands. With a little care and the help of this book, users will be able
to write their own estimation commands—commands that look and behave
just like the official estimation commands in Stata.

Whether you want to fit a special ML estimator for your own research or wish
to write a general-purpose ML estimator for others to use, you need this
book.